Handbook of Latent Variable and Related Models

Handbook of Latent Variable and Related Models
Author: Anonim
Publsiher: Elsevier
Total Pages: 458
Release: 2011-08-11
ISBN 10: 9780080471266
ISBN 13: 0080471269
Language: EN, FR, DE, ES & NL

Handbook of Latent Variable and Related Models Book Review:

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine, public health and sociology. - Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

Handbook of Structural Equation Modeling

Handbook of Structural Equation Modeling
Author: Rick H. Hoyle
Publsiher: Guilford Publications
Total Pages: 740
Release: 2014-01-01
ISBN 10: 1462516793
ISBN 13: 9781462516797
Language: EN, FR, DE, ES & NL

Handbook of Structural Equation Modeling Book Review:

The first comprehensive structural equation modeling (SEM) handbook, this accessible volume presents both the mechanics of SEM and specific SEM strategies and applications. The editor, contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results.

Latent Variable Modeling Using R

Latent Variable Modeling Using R
Author: A. Alexander Beaujean
Publsiher: Routledge
Total Pages: 218
Release: 2014-05-09
ISBN 10: 1317970721
ISBN 13: 9781317970729
Language: EN, FR, DE, ES & NL

Latent Variable Modeling Using R Book Review:

This step-by-step guide is written for R and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the lavaan package, this book is designed to help readers quickly understand LVMs and their analysis in R. The author reviews the reasoning behind the syntax selected and provides examples that demonstrate how to analyze data for a variety of LVMs. Featuring examples applicable to psychology, education, business, and other social and health sciences, minimal text is devoted to theoretical underpinnings. The material is presented without the use of matrix algebra. As a whole the book prepares readers to write about and interpret LVM results they obtain in R. Each chapter features background information, boldfaced key terms defined in the glossary, detailed interpretations of R output, descriptions of how to write the analysis of results for publication, a summary, R based practice exercises (with solutions included in the back of the book), and references and related readings. Margin notes help readers better understand LVMs and write their own R syntax. Examples using data from published work across a variety of disciplines demonstrate how to use R syntax for analyzing and interpreting results. R functions, syntax, and the corresponding results appear in gray boxes to help readers quickly locate this material. A unique index helps readers quickly locate R functions, packages, and datasets. The book and accompanying website at http://blogs.baylor.edu/rlatentvariable/ provides all of the data for the book’s examples and exercises as well as R syntax so readers can replicate the analyses. The book reviews how to enter the data into R, specify the LVMs, and obtain and interpret the estimated parameter values. The book opens with the fundamentals of using R including how to download the program, use functions, and enter and manipulate data. Chapters 2 and 3 introduce and then extend path models to include latent variables. Chapter 4 shows readers how to analyze a latent variable model with data from more than one group, while Chapter 5 shows how to analyze a latent variable model with data from more than one time period. Chapter 6 demonstrates the analysis of dichotomous variables, while Chapter 7 demonstrates how to analyze LVMs with missing data. Chapter 8 focuses on sample size determination using Monte Carlo methods, which can be used with a wide range of statistical models and account for missing data. The final chapter examines hierarchical LVMs, demonstrating both higher-order and bi-factor approaches. The book concludes with three Appendices: a review of common measures of model fit including their formulae and interpretation; syntax for other R latent variable models packages; and solutions for each chapter’s exercises. Intended as a supplementary text for graduate and/or advanced undergraduate courses on latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, business, economics, and social and health sciences, this book also appeals to researchers in these fields. Prerequisites include familiarity with basic statistical concepts, but knowledge of R is not assumed.

Latent Variable Models and Factor Analysis

Latent Variable Models and Factor Analysis
Author: David J. Bartholomew,Martin Knott,Irini Moustaki
Publsiher: John Wiley & Sons
Total Pages: 296
Release: 2011-06-28
ISBN 10: 1119973708
ISBN 13: 9781119973706
Language: EN, FR, DE, ES & NL

Latent Variable Models and Factor Analysis Book Review:

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency. This book: Provides a unified approach showing how such apparently diverse methods as Latent Class Analysis and Factor Analysis are actually members of the same family. Presents new material on ordered manifest variables, MCMC methods, non-linear models as well as a new chapter on related techniques for investigating dependency. Includes new sections on structural equation models (SEM) and Markov Chain Monte Carlo methods for parameter estimation, along with new illustrative examples. Looks at recent developments on goodness-of-fit test statistics and on non-linear models and models with mixed latent variables, both categorical and continuous. No prior acquaintance with latent variable modelling is pre-supposed but a broad understanding of statistical theory will make it easier to see the approach in its proper perspective. Applied statisticians, psychometricians, medical statisticians, biostatisticians, economists and social science researchers will benefit from this book.

Random Effect and Latent Variable Model Selection

Random Effect and Latent Variable Model Selection
Author: David Dunson
Publsiher: Springer Science & Business Media
Total Pages: 170
Release: 2010-03-18
ISBN 10: 9780387767215
ISBN 13: 0387767215
Language: EN, FR, DE, ES & NL

Random Effect and Latent Variable Model Selection Book Review:

Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.

Handbook of Item Response Theory

Handbook of Item Response Theory
Author: Wim J. van der Linden
Publsiher: CRC Press
Total Pages: 1500
Release: 2018-02-19
ISBN 10: 148228247X
ISBN 13: 9781482282474
Language: EN, FR, DE, ES & NL

Handbook of Item Response Theory Book Review:

Drawing on the work of 75 internationally acclaimed experts in the field, Handbook of Item Response Theory, Three-Volume Set presents all major item response models, classical and modern statistical tools used in item response theory (IRT), and major areas of applications of IRT in educational and psychological testing, medical diagnosis of patient-reported outcomes, and marketing research. It also covers CRAN packages, WinBUGS, Bilog MG, Multilog, Parscale, IRTPRO, Mplus, GLLAMM, Latent Gold, and numerous other software tools. A full update of editor Wim J. van der Linden and Ronald K. Hambleton’s classic Handbook of Modern Item Response Theory, this handbook has been expanded from 28 chapters to 85 chapters in three volumes. The three volumes are thoroughly edited and cross-referenced, with uniform notation, format, and pedagogical principles across all chapters. Each chapter is self-contained and deals with the latest developments in IRT.

Structural Equation Modeling

Structural Equation Modeling
Author: Sik-Yum Lee
Publsiher: Wiley
Total Pages: 458
Release: 2007-03-12
ISBN 10: 9780470024232
ISBN 13: 0470024232
Language: EN, FR, DE, ES & NL

Structural Equation Modeling Book Review:

***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances. Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.

Structural Equation Modeling

Structural Equation Modeling
Author: Sik-Yum Lee
Publsiher: John Wiley & Sons
Total Pages: 458
Release: 2007-04-04
ISBN 10: 0470024240
ISBN 13: 9780470024249
Language: EN, FR, DE, ES & NL

Structural Equation Modeling Book Review:

***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances. Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.

Handbook of Data Analysis

Handbook of Data Analysis
Author: Melissa A Hardy,Alan Bryman
Publsiher: SAGE
Total Pages: 728
Release: 2009-06-17
ISBN 10: 1446203441
ISBN 13: 9781446203446
Language: EN, FR, DE, ES & NL

Handbook of Data Analysis Book Review:

Electronic Inspection Copy available for instructors here 'This book provides an excellent reference guide to basic theoretical arguments, practical quantitative techniques and the methodologies that the majority of social science researchers are likely to require for postgraduate study and beyond' - Environment and Planning 'The book provides researchers with guidance in, and examples of, both quantitative and qualitative modes of analysis, written by leading practitioners in the field. The editors give a persuasive account of the commonalities of purpose that exist across both modes, as well as demonstrating a keen awareness of the different things that each offers the practising researcher' - Clive Seale, Brunel University 'With the appearance of this handbook, data analysts no longer have to consult dozens of disparate publications to carry out their work. The essential tools for an intelligent telling of the data story are offered here, in thirty chapters written by recognized experts. ' - Michael Lewis-Beck, F Wendell Miller Distinguished Professor of Political Science, University of Iowa 'This is an excellent guide to current issues in the analysis of social science data. I recommend it to anyone who is looking for authoritative introductions to the state of the art. Each chapter offers a comprehensive review and an extensive bibliography and will be invaluable to researchers wanting to update themselves about modern developments' - Professor Nigel Gilbert, Pro Vice-Chancellor and Professor of Sociology, University of Surrey This is a book that will rapidly be recognized as the bible for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. Scholars and students can turn to it for teaching and applied needs with confidence. The book also seeks to enhance debate in the field by tackling more advanced topics such as models of change, causality, panel models and network analysis. Specialists will find much food for thought in these chapters. A distinctive feature of the book is the breadth of coverage. No other book provides a better one-stop survey of the field of data analysis. In 30 specially commissioned chapters the editors aim to encourage readers to develop an appreciation of the range of analytic options available, so they can choose a research problem and then develop a suitable approach to data analysis.

Handbook of Markov Chain Monte Carlo

Handbook of Markov Chain Monte Carlo
Author: Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng
Publsiher: CRC Press
Total Pages: 619
Release: 2011-05-10
ISBN 10: 1420079425
ISBN 13: 9781420079425
Language: EN, FR, DE, ES & NL

Handbook of Markov Chain Monte Carlo Book Review:

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie

Handbook of Advanced Multilevel Analysis

Handbook of Advanced Multilevel Analysis
Author: Joop Hox,J. Kyle Roberts
Publsiher: Psychology Press
Total Pages: 408
Release: 2011-01-11
ISBN 10: 113695127X
ISBN 13: 9781136951275
Language: EN, FR, DE, ES & NL

Handbook of Advanced Multilevel Analysis Book Review:

This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion. Divided into five sections, the first provides a broad introduction to the field that serves as a framework for understanding the latter chapters. Part 2 focuses on multilevel latent variable modeling including item response theory and mixture modeling. Section 3 addresses models used for longitudinal data including growth curve and structural equation modeling. Special estimation problems are examined in section 4 including the difficulties involved in estimating survival analysis, Bayesian estimation, bootstrapping, multiple imputation, and complicated models, including generalized linear models, optimal design in multilevel models, and more. The book’s concluding section focuses on statistical design issues encountered when doing multilevel modeling including nested designs, analyzing cross-classified models, and dyadic data analysis. Intended for methodologists, statisticians, and researchers in a variety of fields including psychology, education, and the social and health sciences, this handbook also serves as an excellent text for graduate and PhD level courses in multilevel modeling. A basic knowledge of multilevel modeling is assumed.

The SAGE Handbook of Quantitative Methodology for the Social Sciences

The SAGE Handbook of Quantitative Methodology for the Social Sciences
Author: David Kaplan
Publsiher: SAGE
Total Pages: 511
Release: 2004-06-21
ISBN 10: 9780761923596
ISBN 13: 0761923594
Language: EN, FR, DE, ES & NL

The SAGE Handbook of Quantitative Methodology for the Social Sciences Book Review:

The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.

Handbook of Choice Modelling

Handbook of Choice Modelling
Author: Stephane Hess,Andrew Daly
Publsiher: Edward Elgar Publishing
Total Pages: 720
Release: 2014-08-29
ISBN 10: 1781003157
ISBN 13: 9781781003152
Language: EN, FR, DE, ES & NL

Handbook of Choice Modelling Book Review:

The Handbook of Choice Modelling, composed of contributions from senior figures in the field, summarizes the essential analytical techniques and discusses the key current research issues. The book opens with Nobel Laureate Daniel McFadden calling for d

The Psychology Research Handbook

The Psychology Research Handbook
Author: Frederick T. L. Leong,James T. Austin
Publsiher: SAGE
Total Pages: 516
Release: 2006
ISBN 10: 0761930221
ISBN 13: 9780761930228
Language: EN, FR, DE, ES & NL

The Psychology Research Handbook Book Review:

A comprehensive, easy-to-understand guide to the entire research process, this book quickly and efficiently equips advanced students and research assistants to conduct a full-scale investigation. The book is organized around the idea of a 'research script' that is, it follows the standard mode of research planning and design, data collection and analysis, and results writing. The volume contains 35 chapters, some co-authored by advanced graduate students who give their fellow students a touch of the 'real world' adding to the clarity and practicality of many chapters.

The Handbook of Marketing Research

The Handbook of Marketing Research
Author: Rajiv Grover,Marco Vriens
Publsiher: SAGE Publications
Total Pages: 720
Release: 2006-06-23
ISBN 10: 1506319459
ISBN 13: 9781506319452
Language: EN, FR, DE, ES & NL

The Handbook of Marketing Research Book Review:

The Handbook of Marketing Research: Uses, Misuses, and Future Advances comprehensively explores the approaches for delivering market insights for fact-based decision making in a market-oriented firm. Divided into four parts, the Handbook addresses (1) the different nuances of delivering insights; (2) quantitative, qualitative, and online data gathering techniques; (3) basic and advanced data analysis methods; and (4) the substantial marketing issues that clients are interested in resolving through marketing research.

The Oxford Handbook of Panel Data

The Oxford Handbook of Panel Data
Author: Badi H. Baltagi
Publsiher: Oxford University Press
Total Pages: 512
Release: 2014-11-03
ISBN 10: 0190210826
ISBN 13: 9780190210823
Language: EN, FR, DE, ES & NL

The Oxford Handbook of Panel Data Book Review:

The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.

Handbook of Cognitive Aging

Handbook of Cognitive Aging
Author: Scott M. Hofer,Duane F Alwin
Publsiher: SAGE
Total Pages: 744
Release: 2008-03-20
ISBN 10: 145227892X
ISBN 13: 9781452278926
Language: EN, FR, DE, ES & NL

Handbook of Cognitive Aging Book Review:

"Provides a unique perspective. I am particularly impressed with the sections on innovative design and methods to investigate cognitive aging and the integrative perspectives. None of the existing texts covers this material to the same level." —Donna J. La Voie, Saint Louis University "The emphasis on integrating the literature with theoretical and methodological innovations could have a far-reaching impact on the field." —Deb McGinnis, Oakland University The Handbook of Cognitive Aging: Interdisciplinary Perspectives clarifies the differences in patterns and processes of cognitive aging. Along with a comprehensive review of current research, editors Scott M. Hofer and Duane F. Alwin provide a solid foundation for building a multidisciplinary agenda that will stimulate further rigorous research into these complex factors. Key Features Gathers the widest possible range of perspectives by including cognitive aging experts in various disciplines while maintaining a degree of unity across chapters Examines the limitations of the extant literature, particularly in research design and measurement, and offers new suggestions to guide future research Highlights the broad scope of the field with topics ranging from demography to development to neuroscience, offering the most complete coverage available on cognitive aging

Modelling Covariances and Latent Variables Using EQS

Modelling Covariances and Latent Variables Using EQS
Author: G Dunn,Brian S. Everitt,Andrew Pickles
Publsiher: CRC Press
Total Pages: 224
Release: 1993-08-01
ISBN 10: 9780412489907
ISBN 13: 0412489902
Language: EN, FR, DE, ES & NL

Modelling Covariances and Latent Variables Using EQS Book Review:

Handbook of Partial Least Squares

Handbook of Partial Least Squares
Author: Vincenzo Esposito Vinzi,Wynne W. Chin,Jörg Henseler,Huiwen Wang
Publsiher: Springer Science & Business Media
Total Pages: 798
Release: 2010-03-10
ISBN 10: 9783540328278
ISBN 13: 3540328270
Language: EN, FR, DE, ES & NL

Handbook of Partial Least Squares Book Review:

This handbook provides a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to structural equation modeling applications, software and interpretation of results. The handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

The Sage Handbook of Methods in Social Psychology

The Sage Handbook of Methods in Social Psychology
Author: Carol Sansone,Carolyn C Morf,A. T. Panter
Publsiher: SAGE
Total Pages: 528
Release: 2004
ISBN 10: 9780761925354
ISBN 13: 076192535X
Language: EN, FR, DE, ES & NL

The Sage Handbook of Methods in Social Psychology Book Review:

The genius of social psychology as a field has been its ability to investigate the seemingly complicated behaviors that characterize humans as social creatures. The SAGE Handbook of Methods in Social Psychology simplifies this complexity by providing researchers and students with an overview of the rich history of methodological innovation in both basic and applied research within social psychology. This Handbook is a vital resource for behavioral scientists in the academic and research settings who are interested in learning about modern perspectives on classic and innovative methodological approaches in social psychology. Also recommended for undergraduate and graduate students enrolled in social psychology methods courses.